AI query index
The data layer underneath AI visibility: a running record of which domains get cited for which queries, across the three major models.
An AI query index is a continuously refreshed record of which domains get cited or mentioned by AI assistants — ChatGPT, Gemini and Grok — for which queries. Structurally it is an inverted index: a background system runs real buyer questions through the models, extracts the domains each answer cites, and stores the resulting query ↔ domain links along with the model, intent and competitors involved. The index is the data layer that makes everything else in AI visibility possible. Read it forward (query → domains) and you see who gets cited for a question; read it backward (domain → queries) and you get reverse AI search — your AI citation footprint. Because answers change, the index is most valuable when it keeps growing and re-scanning over time rather than taking a single snapshot.
How is an AI query index built?
The build loop has four repeating steps:
- Discover the real buyer queries relevant to a topic or brand.
- Scan each query through ChatGPT, Gemini and Grok.
- Extract the domains each answer cites or mentions, plus the competitors named together.
- Store and re-scan the query ↔ domain links on a cadence, so the index reflects how answers shift, not just a one-time reading.
Why does a shared, growing index matter?
AI answers are volatile — the same question can return different sources from one week to the next. A single snapshot tells you almost nothing about the trend. A persistent index that keeps re-scanning lets you measure AI share of voice over time and catch the moment a competitor displaces you. It also surfaces your ghost routes — pages that should be cited but aren’t.
What can you do with the index?
Three things a flat score cannot support: see the exact queries you win, find the queries a competitor wins and you don’t, and verify every result against a real model answer. The fastest way to query the index yourself is the free Domain Check — it reads the index by domain and returns the live query list across all three models. For the full picture of how the reverse lookup works, see the Reverse AI Search pillar.
Worked example
Suppose the index has scanned “best noise-cancelling headphones,” “headphones for small ears,” and “cheap headphones for the gym.” For each query it stores the domains the model cited — say a review site and two retailers on the first, a forum thread and one retailer on the second. Read forward, the index answers “who gets cited for small-ear headphones?” Read backward by a single retailer’s domain, it returns exactly the queries that retailer is cited on — that flip is reverse AI search in action.
Related terms
- Reverse AI search — reading the index from a domain back to its queries.
- AI citation — the query-to-domain link each index row records.
- AI citation footprint — what you get when you read the index by domain.
Frequently asked questions
What is an AI query index used for?
It powers reverse AI search: instead of asking which domains rank for a query, you ask which queries a given domain gets cited for. It is the data layer behind every AI-visibility metric.
How is an AI query index built?
By repeatedly running many queries against AI models, extracting the domains they cite or mention, and storing those results keyed by a normalized query. Re-running over time turns it into a record of how citations change.